TST: seed the remaining noisy sensor fixtures - #14
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Following @zuorenchen's review note on #13, seed the remaining noisy sensor fixtures (noisy_rotated_accelerometer, noisy_rotated_gyroscope) the same way noisy_barometer already is, so the whole sensor suite is deterministic instead of relying on statistical tolerance bounds. Uses the seed argument added in #13.
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* Add actuator dynamics in TVC and ThrottleControl * Refactor actuator_tau parameters to use None as default in TVC and ThrottleControl * Add actuator dynamics to RollControl * Move actuators to a new actuator folder and rename actuator classes * Refactor roll, throttle, and thrust vector acutator with a new actuator class * Add pytest for all actuators * Update rocket.py and flight.py for actuator class update * Fix actuator class calls * Update active control example with new actuator class * Update time_overshoot in flight.py to align rocketpy commit #45a89 RocketPy-Team@45a891e * Fix actuator unit tests * Enhance ThrustVectorActuator2D with serialization methods and improve documentation * Fix comment msg * Fix pylint unused parameters * Remove unused import * Fix pylint warnings * Refactor __run_in_serial() to fix pylint too much statement warning * Fix: use warning instead of printf * Fix comment * Fix: use warning instead of printf * Run ruff format * Fix thrust3 and effective_thrust name * Fix pytests * Fix pytest utilities * Fix integration/gnss pytest * Update readme * ENH: seed sensor measurement noise per instance (#13) * ENH: seed sensor measurement noise per instance Sensor noise (white noise and random-walk bias drift in InertialSensor and ScalarSensor, plus the GNSS position/velocity accuracy) was drawn from the global numpy RNG. That made it impossible to reproduce from a seed, and unsafe under multiprocess where workers share the global state. Add a seed argument to the sensor constructors and draw all noise from a per-instance numpy Generator. seed=None keeps the random-by-default behaviour but per instance, so it no longer touches the global RNG, matching how StochasticModel and MonteCarlo already seed. A freshly-constructed sensor replays the same sequence, so reproducibility lives at construction rather than in _reset. Adds tests/unit/sensors/test_sensor_seeding.py. * TST: seed the noisy_barometer fixture to fix a pre-existing flake test_noisy_barometer asserts a barometer reading within rel=0.03 of the clean pressure, but the reading carries an unseeded Gaussian noise draw plus a +1000 Pa constant_bias the expected value omits, leaving only ~+2.42 sigma of headroom, so it already flaked at ~0.76% on develop (a 20k-trial check: develop 0.77%, this branch 0.75%). Passing a fixed seed makes it deterministic and also exercises the new sensor seed argument. * TST: seed the noisy accelerometer and gyroscope fixtures (#14) Following @zuorenchen's review note on #13, seed the remaining noisy sensor fixtures (noisy_rotated_accelerometer, noisy_rotated_gyroscope) the same way noisy_barometer already is, so the whole sensor suite is deterministic instead of relying on statistical tolerance bounds. Uses the seed argument added in #13. * ENH: pull v1.13 from RocketPy (#16) * test: characterization tests for Flight.step_simulation() (#11) * test: characterization tests for Flight.step_simulation() Verify the stepped-simulation API (run_simulation=False plus repeated step_simulation() calls) reproduces a one-shot simulate(): - initial step state is unfinished at the first phase - stepping visits multiple phases and reaches the finished state - the stepped trajectory (final t and the full solution array) matches simulate() to a tight tolerance, robust to LSODA last-bit noise across platforms - post_process_simulation / initialize_prints_plots fire on finish (t_final, prints, plots present) - stepping after finished is a no-op Uses the parachute-free calisto flight (parachute triggers are not migrated into the stepping path); the twin's launch parameters are read back from the reference so it cannot drift. Scope: uncontrolled stepping only. * test: cover controlled stepping in step_simulation() Inject a roll command between step_simulation() calls (the Balloon Popping Challenge use case) and assert the trajectory responds: - a sustained command spins the body up (roll rate w3 grows) while a neutral command leaves w3 at zero - a mid-flight command reversal turns the roll rate around -- only possible if the command is re-read every step (a latched command could not) - a zero command leaves the angular state bit-identical to an uncontrolled run, isolating the divergence as the command, not the actuator's presence Uses time_overshoot=False (as BPC does) so each step advances one solver node and the command is injected per timestep. Tolerances/thresholds are used rather than bit-exact equality, to stay robust to LSODA last-bit noise across platforms. --------- Co-authored-by: RickyRicato <rickywang44@gmail.com> Co-authored-by: ChiChun Wang <99960850+chichunwang@users.noreply.github.com> Co-authored-by: 秀吉 <84045975+thc1006@users.noreply.github.com>
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Follow-up to #13, addressing @zuorenchen's review note: seed the other two noisy sensor fixtures (noisy_rotated_accelerometer, noisy_rotated_gyroscope) the same way noisy_barometer already is, so the whole sensor test suite is deterministic instead of relying on loose statistical tolerances. Uses the per-sensor seed argument added in #13.
Verified locally: both fixtures' tests pass deterministically (run repeatedly), the full tests/unit/sensors suite is 42 passed, ruff + pylint clean (10.00/10).